{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> UNSW-NB15 Dataset Preprocessing Notebook <center/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas_profiling , pickle\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from preprocessing import get_data\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler,power_transform\n",
    "from scipy.stats import skew"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data and do Profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test, label_mapping = get_data()\n",
    "\n",
    "profile = pandas_profiling.ProfileReport(train)\n",
    "profile.to_file(\"profile_report.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ct_src_dport_ltm',\n",
       " 'ct_srv_dst',\n",
       " 'ct_srv_src',\n",
       " 'dloss',\n",
       " 'dpkts',\n",
       " 'is_ftp_login',\n",
       " 'sinpkt',\n",
       " 'sloss',\n",
       " 'spkts',\n",
       " 'swin',\n",
       " 'tcprtt']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.get_rejected_variables(.90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove rejected features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_remove = profile.get_rejected_variables(.95)\n",
    "train.drop(to_remove, axis=1, inplace=True)\n",
    "\n",
    "data ,y_train = train.drop([\"label\",\"attack_cat\"],axis=1),train.attack_cat.values\n",
    "\n",
    "label_cols = ['label']\n",
    "data_cols = list(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_cols)\n",
    "np.unique(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Plot_data_features(data_cols, X, y = None):\n",
    "    \"\"\"\"Helper method to plot Dataset features distributions using histograms\"\"\"\n",
    "    axarr = [[]]*len(data_cols)\n",
    "    columns = 4\n",
    "    rows = int( np.ceil( len(data_cols) / columns ) )\n",
    "    f, fig = plt.subplots( figsize=(columns*3.5, rows*2) )\n",
    "\n",
    "    f.suptitle('Data Distributions by Feature and Class', size=16)\n",
    "\n",
    "    for i, col in enumerate(data_cols[:]):\n",
    "        axarr[i] = plt.subplot2grid( (int(rows), int(columns)), (int(i//columns), int(i%columns)) )\n",
    "        axarr[i].hist( [ X.loc[ y == 1, col ], X.loc[ y != 1, col ] ], label=['Normal','Attack'], \n",
    "                              bins=np.linspace( np.percentile(X[col],0.1), np.percentile(X[col],99.9), 30 ),\n",
    "                              normed=True )\n",
    "        axarr[i].set_xlabel(col, size=12)\n",
    "        axarr[i].set_ylim([0,0.8])\n",
    "        axarr[i].tick_params(axis='both', labelsize=10)\n",
    "        if i == 0: \n",
    "            legend = axarr[i].legend()\n",
    "            legend.get_frame().set_facecolor('white')\n",
    "        if i%4 != 0 : \n",
    "            axarr[i].tick_params(axis='y', left='off', labelleft='off')\n",
    "        else:\n",
    "            axarr[i].set_ylabel('Fraction',size=12)\n",
    "\n",
    "    plt.tight_layout(rect=[0,0,1,0.95]) \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <center> Scale & Transform Data <center/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Robust Scaler\n",
    "Scale features using statistics that are robust to outliers. \n",
    "This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 10th quantile and the 3rd quartile 99th quantile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dur</th>\n",
       "      <th>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "      <th>sbytes</th>\n",
       "      <th>dbytes</th>\n",
       "      <th>rate</th>\n",
       "      <th>sttl</th>\n",
       "      <th>dttl</th>\n",
       "      <th>sload</th>\n",
       "      <th>...</th>\n",
       "      <th>response_body_len</th>\n",
       "      <th>ct_state_ttl</th>\n",
       "      <th>ct_dst_ltm</th>\n",
       "      <th>ct_src_dport_ltm</th>\n",
       "      <th>ct_dst_sport_ltm</th>\n",
       "      <th>ct_dst_src_ltm</th>\n",
       "      <th>ct_ftp_cmd</th>\n",
       "      <th>ct_flw_http_mthd</th>\n",
       "      <th>ct_src_ltm</th>\n",
       "      <th>is_sm_ips_ports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>-0.000192</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>-0.003152</td>\n",
       "      <td>-0.007874</td>\n",
       "      <td>0.892857</td>\n",
       "      <td>-0.000406</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>-0.022727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.044444</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010809</td>\n",
       "      <td>-0.007634</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>0.038227</td>\n",
       "      <td>-0.003147</td>\n",
       "      <td>-0.755906</td>\n",
       "      <td>0.884921</td>\n",
       "      <td>-0.000409</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.022727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.044444</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.027035</td>\n",
       "      <td>-0.007634</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.000074</td>\n",
       "      <td>0.011895</td>\n",
       "      <td>-0.003212</td>\n",
       "      <td>-0.755906</td>\n",
       "      <td>0.884921</td>\n",
       "      <td>-0.000412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.022222</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.028011</td>\n",
       "      <td>-0.007634</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>-0.003212</td>\n",
       "      <td>-0.755906</td>\n",
       "      <td>0.884921</td>\n",
       "      <td>-0.000411</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.022222</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.007467</td>\n",
       "      <td>-0.007634</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>-0.003192</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.884921</td>\n",
       "      <td>-0.000409</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.022222</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        dur     proto   service  state    sbytes    dbytes      rate  \\\n",
       "0  0.001999 -0.007634  0.000000  -0.25 -0.000192  0.000007 -0.003152   \n",
       "1  0.010809 -0.007634  0.000000  -0.25  0.000339  0.038227 -0.003147   \n",
       "2  0.027035 -0.007634  0.000000  -0.25 -0.000074  0.011895 -0.003212   \n",
       "3  0.028011 -0.007634  0.111111  -0.25  0.000221  0.000554 -0.003212   \n",
       "4  0.007467 -0.007634  0.000000  -0.25  0.000116  0.000095 -0.003192   \n",
       "\n",
       "       sttl      dttl     sload  ...  response_body_len  ct_state_ttl  \\\n",
       "0 -0.007874  0.892857 -0.000406  ...                0.0     -0.166667   \n",
       "1 -0.755906  0.884921 -0.000409  ...                0.0      0.000000   \n",
       "2 -0.755906  0.884921 -0.000412  ...                0.0      0.000000   \n",
       "3 -0.755906  0.884921 -0.000411  ...                0.0      0.000000   \n",
       "4  0.000000  0.884921 -0.000409  ...                0.0      0.000000   \n",
       "\n",
       "   ct_dst_ltm  ct_src_dport_ltm  ct_dst_sport_ltm  ct_dst_src_ltm  ct_ftp_cmd  \\\n",
       "0   -0.022727          0.000000               0.0           -0.04         0.0   \n",
       "1   -0.022727          0.000000               0.0           -0.02         0.0   \n",
       "2    0.000000          0.000000               0.0            0.00         0.0   \n",
       "3    0.000000          0.000000               0.0            0.00         1.0   \n",
       "4    0.000000          0.022727               0.0            0.74         0.0   \n",
       "\n",
       "   ct_flw_http_mthd  ct_src_ltm  is_sm_ips_ports  \n",
       "0               0.0   -0.044444              0.0  \n",
       "1               0.0   -0.044444              0.0  \n",
       "2               0.0   -0.022222              0.0  \n",
       "3               0.0   -0.022222              0.0  \n",
       "4               0.0   -0.022222              0.0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_Robust_trans = pd.DataFrame(data=RobustScaler(quantile_range=(0.1, 99.9 )).fit_transform(data[data_cols].values),columns=list(data.columns))\n",
    "data_Robust_trans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1296 with 34 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_data_features(X=data_Robust_trans,data_cols=data_cols,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = data_Robust_trans.quantile(0.05)\n",
    "Q3 = data_Robust_trans.quantile(0.95)\n",
    "IQR = Q3 - Q1\n",
    "# print(IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = ~((data_Robust_trans < (Q1 - 1.5 * IQR)) | \\\n",
    "         (data_Robust_trans > (Q3 + 1.5 * IQR))).any(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1296 with 34 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_data_features(X=data_Robust_trans[mask],data_cols=data_cols,y=y_train[mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_Robust_trans[\"label\"] = y_train \n",
    "with open('../Data/UNSW-NB15/data_Robust_trans.pickle', 'wb') as f:\n",
    "    pickle.dump(data_Robust_trans,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Power Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dur</th>\n",
       "      <th>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "      <th>spkts</th>\n",
       "      <th>dpkts</th>\n",
       "      <th>rate</th>\n",
       "      <th>sttl</th>\n",
       "      <th>dttl</th>\n",
       "      <th>sload</th>\n",
       "      <th>...</th>\n",
       "      <th>smean</th>\n",
       "      <th>dmean</th>\n",
       "      <th>trans_depth</th>\n",
       "      <th>response_body_len</th>\n",
       "      <th>ct_srv_src</th>\n",
       "      <th>ct_state_ttl</th>\n",
       "      <th>ct_dst_ltm</th>\n",
       "      <th>is_ftp_login</th>\n",
       "      <th>ct_flw_http_mthd</th>\n",
       "      <th>ct_src_ltm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.198694</td>\n",
       "      <td>-0.91809</td>\n",
       "      <td>-0.904939</td>\n",
       "      <td>-1.071008</td>\n",
       "      <td>0.222781</td>\n",
       "      <td>0.386101</td>\n",
       "      <td>-0.886597</td>\n",
       "      <td>0.704082</td>\n",
       "      <td>1.259365</td>\n",
       "      <td>-0.986806</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.953675</td>\n",
       "      <td>0.587295</td>\n",
       "      <td>-0.336555</td>\n",
       "      <td>-0.262385</td>\n",
       "      <td>-1.370661</td>\n",
       "      <td>-1.515026</td>\n",
       "      <td>-1.145736</td>\n",
       "      <td>-0.121892</td>\n",
       "      <td>-0.336555</td>\n",
       "      <td>-1.279673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.995349</td>\n",
       "      <td>-0.91809</td>\n",
       "      <td>-0.904939</td>\n",
       "      <td>-1.071008</td>\n",
       "      <td>0.882238</td>\n",
       "      <td>1.383878</td>\n",
       "      <td>-0.874725</td>\n",
       "      <td>-1.124046</td>\n",
       "      <td>1.256852</td>\n",
       "      <td>-1.075737</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.140630</td>\n",
       "      <td>1.615158</td>\n",
       "      <td>-0.336555</td>\n",
       "      <td>-0.262385</td>\n",
       "      <td>1.740536</td>\n",
       "      <td>-0.224801</td>\n",
       "      <td>-1.145736</td>\n",
       "      <td>-0.121892</td>\n",
       "      <td>-0.336555</td>\n",
       "      <td>-1.279673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.618843</td>\n",
       "      <td>-0.91809</td>\n",
       "      <td>-0.904939</td>\n",
       "      <td>-1.071008</td>\n",
       "      <td>0.470300</td>\n",
       "      <td>1.054254</td>\n",
       "      <td>-1.209509</td>\n",
       "      <td>-1.124046</td>\n",
       "      <td>1.256852</td>\n",
       "      <td>-1.343700</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.644611</td>\n",
       "      <td>1.531983</td>\n",
       "      <td>-0.336555</td>\n",
       "      <td>-0.262385</td>\n",
       "      <td>0.361515</td>\n",
       "      <td>-0.224801</td>\n",
       "      <td>-0.351918</td>\n",
       "      <td>-0.121892</td>\n",
       "      <td>-0.336555</td>\n",
       "      <td>-0.560046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.636531</td>\n",
       "      <td>-0.91809</td>\n",
       "      <td>-0.078885</td>\n",
       "      <td>-1.071008</td>\n",
       "      <td>0.777863</td>\n",
       "      <td>0.928489</td>\n",
       "      <td>-1.215951</td>\n",
       "      <td>-1.124046</td>\n",
       "      <td>1.256852</td>\n",
       "      <td>-1.257635</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.140630</td>\n",
       "      <td>0.726105</td>\n",
       "      <td>-0.336555</td>\n",
       "      <td>-0.262385</td>\n",
       "      <td>-1.370661</td>\n",
       "      <td>-0.224801</td>\n",
       "      <td>-0.351918</td>\n",
       "      <td>8.204011</td>\n",
       "      <td>-0.336555</td>\n",
       "      <td>-0.560046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.691451</td>\n",
       "      <td>-0.91809</td>\n",
       "      <td>-0.904939</td>\n",
       "      <td>-1.071008</td>\n",
       "      <td>0.645553</td>\n",
       "      <td>0.595491</td>\n",
       "      <td>-1.047114</td>\n",
       "      <td>0.722657</td>\n",
       "      <td>1.256852</td>\n",
       "      <td>-1.072445</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.068528</td>\n",
       "      <td>0.603326</td>\n",
       "      <td>-0.336555</td>\n",
       "      <td>-0.262385</td>\n",
       "      <td>1.740536</td>\n",
       "      <td>-0.224801</td>\n",
       "      <td>-0.351918</td>\n",
       "      <td>-0.121892</td>\n",
       "      <td>-0.336555</td>\n",
       "      <td>-0.560046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        dur    proto   service     state     spkts     dpkts      rate  \\\n",
       "0 -0.198694 -0.91809 -0.904939 -1.071008  0.222781  0.386101 -0.886597   \n",
       "1  0.995349 -0.91809 -0.904939 -1.071008  0.882238  1.383878 -0.874725   \n",
       "2  1.618843 -0.91809 -0.904939 -1.071008  0.470300  1.054254 -1.209509   \n",
       "3  1.636531 -0.91809 -0.078885 -1.071008  0.777863  0.928489 -1.215951   \n",
       "4  0.691451 -0.91809 -0.904939 -1.071008  0.645553  0.595491 -1.047114   \n",
       "\n",
       "       sttl      dttl     sload     ...         smean     dmean  trans_depth  \\\n",
       "0  0.704082  1.259365 -0.986806     ...     -1.953675  0.587295    -0.336555   \n",
       "1 -1.124046  1.256852 -1.075737     ...     -1.140630  1.615158    -0.336555   \n",
       "2 -1.124046  1.256852 -1.343700     ...     -1.644611  1.531983    -0.336555   \n",
       "3 -1.124046  1.256852 -1.257635     ...     -1.140630  0.726105    -0.336555   \n",
       "4  0.722657  1.256852 -1.072445     ...     -1.068528  0.603326    -0.336555   \n",
       "\n",
       "   response_body_len  ct_srv_src  ct_state_ttl  ct_dst_ltm  is_ftp_login  \\\n",
       "0          -0.262385   -1.370661     -1.515026   -1.145736     -0.121892   \n",
       "1          -0.262385    1.740536     -0.224801   -1.145736     -0.121892   \n",
       "2          -0.262385    0.361515     -0.224801   -0.351918     -0.121892   \n",
       "3          -0.262385   -1.370661     -0.224801   -0.351918      8.204011   \n",
       "4          -0.262385    1.740536     -0.224801   -0.351918     -0.121892   \n",
       "\n",
       "   ct_flw_http_mthd  ct_src_ltm  \n",
       "0         -0.336555   -1.279673  \n",
       "1         -0.336555   -1.279673  \n",
       "2         -0.336555   -0.560046  \n",
       "3         -0.336555   -0.560046  \n",
       "4         -0.336555   -0.560046  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_power_trans = pd.DataFrame(data=power_transform(X=data.values,method=\"yeo-johnson\"),columns=list(data.columns))\n",
    "# data_power_trans.describe()\n",
    "data_power_trans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1600 with 29 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_data_features(X=data_power_trans,data_cols=data_cols,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dump New dataset to pickle file\n",
    "data_power_trans[\"label\"] = y_train \n",
    "with open('../Data/UNSW-NB15/data_power_trans.pickle', 'wb') as f:\n",
    "    pickle.dump(data_power_trans,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dur</th>\n",
       "      <th>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "      <th>spkts</th>\n",
       "      <th>dpkts</th>\n",
       "      <th>rate</th>\n",
       "      <th>sttl</th>\n",
       "      <th>dttl</th>\n",
       "      <th>sload</th>\n",
       "      <th>...</th>\n",
       "      <th>smean</th>\n",
       "      <th>dmean</th>\n",
       "      <th>trans_depth</th>\n",
       "      <th>response_body_len</th>\n",
       "      <th>ct_srv_src</th>\n",
       "      <th>ct_state_ttl</th>\n",
       "      <th>ct_dst_ltm</th>\n",
       "      <th>is_ftp_login</th>\n",
       "      <th>ct_flw_http_mthd</th>\n",
       "      <th>ct_src_ltm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.191029</td>\n",
       "      <td>-0.352112</td>\n",
       "      <td>-0.857585</td>\n",
       "      <td>-0.926459</td>\n",
       "      <td>-0.104456</td>\n",
       "      <td>-0.135769</td>\n",
       "      <td>-0.576371</td>\n",
       "      <td>0.703839</td>\n",
       "      <td>1.578100</td>\n",
       "      <td>-0.389897</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.458048</td>\n",
       "      <td>-0.314240</td>\n",
       "      <td>-0.136415</td>\n",
       "      <td>-0.039557</td>\n",
       "      <td>-0.775991</td>\n",
       "      <td>-1.366486</td>\n",
       "      <td>-0.645013</td>\n",
       "      <td>-0.118590</td>\n",
       "      <td>-0.189768</td>\n",
       "      <td>-0.715714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.109485</td>\n",
       "      <td>-0.352112</td>\n",
       "      <td>-0.857585</td>\n",
       "      <td>-0.926459</td>\n",
       "      <td>-0.046014</td>\n",
       "      <td>0.172599</td>\n",
       "      <td>-0.576345</td>\n",
       "      <td>-1.141901</td>\n",
       "      <td>1.560002</td>\n",
       "      <td>-0.389928</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.414076</td>\n",
       "      <td>3.800869</td>\n",
       "      <td>-0.136415</td>\n",
       "      <td>-0.039557</td>\n",
       "      <td>3.147666</td>\n",
       "      <td>-0.318711</td>\n",
       "      <td>-0.645013</td>\n",
       "      <td>-0.118590</td>\n",
       "      <td>-0.189768</td>\n",
       "      <td>-0.715714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.040699</td>\n",
       "      <td>-0.352112</td>\n",
       "      <td>-0.857585</td>\n",
       "      <td>-0.926459</td>\n",
       "      <td>-0.089845</td>\n",
       "      <td>-0.026933</td>\n",
       "      <td>-0.576734</td>\n",
       "      <td>-1.141901</td>\n",
       "      <td>1.560002</td>\n",
       "      <td>-0.389964</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.443391</td>\n",
       "      <td>2.709185</td>\n",
       "      <td>-0.136415</td>\n",
       "      <td>-0.039557</td>\n",
       "      <td>-0.215468</td>\n",
       "      <td>-0.318711</td>\n",
       "      <td>-0.520827</td>\n",
       "      <td>-0.118590</td>\n",
       "      <td>-0.189768</td>\n",
       "      <td>-0.595543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049729</td>\n",
       "      <td>-0.352112</td>\n",
       "      <td>-0.493872</td>\n",
       "      <td>-0.926459</td>\n",
       "      <td>-0.060624</td>\n",
       "      <td>-0.063212</td>\n",
       "      <td>-0.576737</td>\n",
       "      <td>-1.141901</td>\n",
       "      <td>1.560002</td>\n",
       "      <td>-0.389958</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.414076</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>-0.136415</td>\n",
       "      <td>-0.039557</td>\n",
       "      <td>-0.775991</td>\n",
       "      <td>-0.318711</td>\n",
       "      <td>-0.520827</td>\n",
       "      <td>7.814915</td>\n",
       "      <td>-0.189768</td>\n",
       "      <td>-0.595543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.140417</td>\n",
       "      <td>-0.352112</td>\n",
       "      <td>-0.857585</td>\n",
       "      <td>-0.926459</td>\n",
       "      <td>-0.075235</td>\n",
       "      <td>-0.117630</td>\n",
       "      <td>-0.576617</td>\n",
       "      <td>0.723268</td>\n",
       "      <td>1.560002</td>\n",
       "      <td>-0.389927</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.409190</td>\n",
       "      <td>-0.306498</td>\n",
       "      <td>-0.136415</td>\n",
       "      <td>-0.039557</td>\n",
       "      <td>3.147666</td>\n",
       "      <td>-0.318711</td>\n",
       "      <td>-0.520827</td>\n",
       "      <td>-0.118590</td>\n",
       "      <td>-0.189768</td>\n",
       "      <td>-0.595543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        dur     proto   service     state     spkts     dpkts      rate  \\\n",
       "0 -0.191029 -0.352112 -0.857585 -0.926459 -0.104456 -0.135769 -0.576371   \n",
       "1 -0.109485 -0.352112 -0.857585 -0.926459 -0.046014  0.172599 -0.576345   \n",
       "2  0.040699 -0.352112 -0.857585 -0.926459 -0.089845 -0.026933 -0.576734   \n",
       "3  0.049729 -0.352112 -0.493872 -0.926459 -0.060624 -0.063212 -0.576737   \n",
       "4 -0.140417 -0.352112 -0.857585 -0.926459 -0.075235 -0.117630 -0.576617   \n",
       "\n",
       "       sttl      dttl     sload     ...         smean     dmean  trans_depth  \\\n",
       "0  0.703839  1.578100 -0.389897     ...     -0.458048 -0.314240    -0.136415   \n",
       "1 -1.141901  1.560002 -0.389928     ...     -0.414076  3.800869    -0.136415   \n",
       "2 -1.141901  1.560002 -0.389964     ...     -0.443391  2.709185    -0.136415   \n",
       "3 -1.141901  1.560002 -0.389958     ...     -0.414076 -0.232945    -0.136415   \n",
       "4  0.723268  1.560002 -0.389927     ...     -0.409190 -0.306498    -0.136415   \n",
       "\n",
       "   response_body_len  ct_srv_src  ct_state_ttl  ct_dst_ltm  is_ftp_login  \\\n",
       "0          -0.039557   -0.775991     -1.366486   -0.645013     -0.118590   \n",
       "1          -0.039557    3.147666     -0.318711   -0.645013     -0.118590   \n",
       "2          -0.039557   -0.215468     -0.318711   -0.520827     -0.118590   \n",
       "3          -0.039557   -0.775991     -0.318711   -0.520827      7.814915   \n",
       "4          -0.039557    3.147666     -0.318711   -0.520827     -0.118590   \n",
       "\n",
       "   ct_flw_http_mthd  ct_src_ltm  \n",
       "0         -0.189768   -0.715714  \n",
       "1         -0.189768   -0.715714  \n",
       "2         -0.189768   -0.595543  \n",
       "3         -0.189768   -0.595543  \n",
       "4         -0.189768   -0.595543  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_std = StandardScaler().fit_transform(data.values)\n",
    "data_std = pd.DataFrame(data=data_std,columns=list(data.columns))\n",
    "data_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dur</th>\n",
       "      <th>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "      <th>spkts</th>\n",
       "      <th>dpkts</th>\n",
       "      <th>rate</th>\n",
       "      <th>sttl</th>\n",
       "      <th>dttl</th>\n",
       "      <th>sload</th>\n",
       "      <th>...</th>\n",
       "      <th>smean</th>\n",
       "      <th>dmean</th>\n",
       "      <th>trans_depth</th>\n",
       "      <th>response_body_len</th>\n",
       "      <th>ct_srv_src</th>\n",
       "      <th>ct_state_ttl</th>\n",
       "      <th>ct_dst_ltm</th>\n",
       "      <th>is_ftp_login</th>\n",
       "      <th>ct_flw_http_mthd</th>\n",
       "      <th>ct_src_ltm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "      <td>1.753410e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-3.566066e-18</td>\n",
       "      <td>8.688234e-17</td>\n",
       "      <td>5.705706e-17</td>\n",
       "      <td>1.945127e-16</td>\n",
       "      <td>-1.426426e-17</td>\n",
       "      <td>5.187005e-18</td>\n",
       "      <td>-3.241878e-17</td>\n",
       "      <td>-1.607972e-16</td>\n",
       "      <td>-1.141141e-16</td>\n",
       "      <td>1.167076e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.491264e-17</td>\n",
       "      <td>-1.815452e-17</td>\n",
       "      <td>-2.139640e-17</td>\n",
       "      <td>9.117783e-18</td>\n",
       "      <td>4.408955e-17</td>\n",
       "      <td>2.593503e-17</td>\n",
       "      <td>1.115206e-16</td>\n",
       "      <td>-6.483757e-18</td>\n",
       "      <td>-5.187005e-18</td>\n",
       "      <td>3.630904e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "      <td>1.000003e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.097747e-01</td>\n",
       "      <td>-3.521120e-01</td>\n",
       "      <td>-8.575849e-01</td>\n",
       "      <td>-9.264591e-01</td>\n",
       "      <td>-1.409822e-01</td>\n",
       "      <td>-1.720474e-01</td>\n",
       "      <td>-5.768192e-01</td>\n",
       "      <td>-1.744196e+00</td>\n",
       "      <td>-7.204059e-01</td>\n",
       "      <td>-3.899726e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.313342e-01</td>\n",
       "      <td>-4.807028e-01</td>\n",
       "      <td>-1.364151e-01</td>\n",
       "      <td>-3.955687e-02</td>\n",
       "      <td>-7.759906e-01</td>\n",
       "      <td>-1.366486e+00</td>\n",
       "      <td>-6.450129e-01</td>\n",
       "      <td>-1.185902e-01</td>\n",
       "      <td>-1.897681e-01</td>\n",
       "      <td>-7.157137e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-2.097735e-01</td>\n",
       "      <td>-3.521120e-01</td>\n",
       "      <td>-8.575849e-01</td>\n",
       "      <td>-9.264591e-01</td>\n",
       "      <td>-1.336769e-01</td>\n",
       "      <td>-1.720474e-01</td>\n",
       "      <td>-5.766210e-01</td>\n",
       "      <td>-1.141901e+00</td>\n",
       "      <td>-7.204059e-01</td>\n",
       "      <td>-3.899033e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.896474e-01</td>\n",
       "      <td>-4.807028e-01</td>\n",
       "      <td>-1.364151e-01</td>\n",
       "      <td>-3.955687e-02</td>\n",
       "      <td>-6.825702e-01</td>\n",
       "      <td>-3.187109e-01</td>\n",
       "      <td>-6.450129e-01</td>\n",
       "      <td>-1.185902e-01</td>\n",
       "      <td>-1.897681e-01</td>\n",
       "      <td>-5.955426e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.095306e-01</td>\n",
       "      <td>-3.054935e-01</td>\n",
       "      <td>-8.575849e-01</td>\n",
       "      <td>4.617925e-01</td>\n",
       "      <td>-1.336769e-01</td>\n",
       "      <td>-1.539081e-01</td>\n",
       "      <td>-5.573162e-01</td>\n",
       "      <td>7.232680e-01</td>\n",
       "      <td>-4.579780e-01</td>\n",
       "      <td>-3.853023e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.114753e-01</td>\n",
       "      <td>-3.103690e-01</td>\n",
       "      <td>-1.364151e-01</td>\n",
       "      <td>-3.955687e-02</td>\n",
       "      <td>-4.023091e-01</td>\n",
       "      <td>-3.187109e-01</td>\n",
       "      <td>-5.208272e-01</td>\n",
       "      <td>-1.185902e-01</td>\n",
       "      <td>-1.897681e-01</td>\n",
       "      <td>-4.753715e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-1.066813e-01</td>\n",
       "      <td>-3.054935e-01</td>\n",
       "      <td>1.324692e+00</td>\n",
       "      <td>4.617925e-01</td>\n",
       "      <td>-6.062410e-02</td>\n",
       "      <td>-8.135096e-02</td>\n",
       "      <td>1.789221e-01</td>\n",
       "      <td>7.232680e-01</td>\n",
       "      <td>1.560002e+00</td>\n",
       "      <td>8.194472e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.795600e-01</td>\n",
       "      <td>-1.361640e-01</td>\n",
       "      <td>-1.364151e-01</td>\n",
       "      <td>-3.955687e-02</td>\n",
       "      <td>2.516337e-01</td>\n",
       "      <td>7.290641e-01</td>\n",
       "      <td>1.001016e-01</td>\n",
       "      <td>-1.185902e-01</td>\n",
       "      <td>-1.897681e-01</td>\n",
       "      <td>2.456551e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.049154e+00</td>\n",
       "      <td>5.801534e+00</td>\n",
       "      <td>3.506969e+00</td>\n",
       "      <td>1.017955e+01</td>\n",
       "      <td>7.009933e+01</td>\n",
       "      <td>9.935819e+01</td>\n",
       "      <td>5.469112e+00</td>\n",
       "      <td>7.329824e-01</td>\n",
       "      <td>1.578100e+00</td>\n",
       "      <td>3.140074e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>6.680036e+00</td>\n",
       "      <td>5.163540e+00</td>\n",
       "      <td>2.212539e+02</td>\n",
       "      <td>1.209403e+02</td>\n",
       "      <td>5.016074e+00</td>\n",
       "      <td>4.920164e+00</td>\n",
       "      <td>5.564275e+00</td>\n",
       "      <td>3.161543e+01</td>\n",
       "      <td>4.259369e+01</td>\n",
       "      <td>6.374381e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                dur         proto       service         state         spkts  \\\n",
       "count  1.753410e+05  1.753410e+05  1.753410e+05  1.753410e+05  1.753410e+05   \n",
       "mean  -3.566066e-18  8.688234e-17  5.705706e-17  1.945127e-16 -1.426426e-17   \n",
       "std    1.000003e+00  1.000003e+00  1.000003e+00  1.000003e+00  1.000003e+00   \n",
       "min   -2.097747e-01 -3.521120e-01 -8.575849e-01 -9.264591e-01 -1.409822e-01   \n",
       "25%   -2.097735e-01 -3.521120e-01 -8.575849e-01 -9.264591e-01 -1.336769e-01   \n",
       "50%   -2.095306e-01 -3.054935e-01 -8.575849e-01  4.617925e-01 -1.336769e-01   \n",
       "75%   -1.066813e-01 -3.054935e-01  1.324692e+00  4.617925e-01 -6.062410e-02   \n",
       "max    9.049154e+00  5.801534e+00  3.506969e+00  1.017955e+01  7.009933e+01   \n",
       "\n",
       "              dpkts          rate          sttl          dttl         sload  \\\n",
       "count  1.753410e+05  1.753410e+05  1.753410e+05  1.753410e+05  1.753410e+05   \n",
       "mean   5.187005e-18 -3.241878e-17 -1.607972e-16 -1.141141e-16  1.167076e-17   \n",
       "std    1.000003e+00  1.000003e+00  1.000003e+00  1.000003e+00  1.000003e+00   \n",
       "min   -1.720474e-01 -5.768192e-01 -1.744196e+00 -7.204059e-01 -3.899726e-01   \n",
       "25%   -1.720474e-01 -5.766210e-01 -1.141901e+00 -7.204059e-01 -3.899033e-01   \n",
       "50%   -1.539081e-01 -5.573162e-01  7.232680e-01 -4.579780e-01 -3.853023e-01   \n",
       "75%   -8.135096e-02  1.789221e-01  7.232680e-01  1.560002e+00  8.194472e-02   \n",
       "max    9.935819e+01  5.469112e+00  7.329824e-01  1.578100e+00  3.140074e+01   \n",
       "\n",
       "           ...              smean         dmean   trans_depth  \\\n",
       "count      ...       1.753410e+05  1.753410e+05  1.753410e+05   \n",
       "mean       ...      -1.491264e-17 -1.815452e-17 -2.139640e-17   \n",
       "std        ...       1.000003e+00  1.000003e+00  1.000003e+00   \n",
       "min        ...      -5.313342e-01 -4.807028e-01 -1.364151e-01   \n",
       "25%        ...      -3.896474e-01 -4.807028e-01 -1.364151e-01   \n",
       "50%        ...      -3.114753e-01 -3.103690e-01 -1.364151e-01   \n",
       "75%        ...      -1.795600e-01 -1.361640e-01 -1.364151e-01   \n",
       "max        ...       6.680036e+00  5.163540e+00  2.212539e+02   \n",
       "\n",
       "       response_body_len    ct_srv_src  ct_state_ttl    ct_dst_ltm  \\\n",
       "count       1.753410e+05  1.753410e+05  1.753410e+05  1.753410e+05   \n",
       "mean        9.117783e-18  4.408955e-17  2.593503e-17  1.115206e-16   \n",
       "std         1.000003e+00  1.000003e+00  1.000003e+00  1.000003e+00   \n",
       "min        -3.955687e-02 -7.759906e-01 -1.366486e+00 -6.450129e-01   \n",
       "25%        -3.955687e-02 -6.825702e-01 -3.187109e-01 -6.450129e-01   \n",
       "50%        -3.955687e-02 -4.023091e-01 -3.187109e-01 -5.208272e-01   \n",
       "75%        -3.955687e-02  2.516337e-01  7.290641e-01  1.001016e-01   \n",
       "max         1.209403e+02  5.016074e+00  4.920164e+00  5.564275e+00   \n",
       "\n",
       "       is_ftp_login  ct_flw_http_mthd    ct_src_ltm  \n",
       "count  1.753410e+05      1.753410e+05  1.753410e+05  \n",
       "mean  -6.483757e-18     -5.187005e-18  3.630904e-17  \n",
       "std    1.000003e+00      1.000003e+00  1.000003e+00  \n",
       "min   -1.185902e-01     -1.897681e-01 -7.157137e-01  \n",
       "25%   -1.185902e-01     -1.897681e-01 -5.955426e-01  \n",
       "50%   -1.185902e-01     -1.897681e-01 -4.753715e-01  \n",
       "75%   -1.185902e-01     -1.897681e-01  2.456551e-01  \n",
       "max    3.161543e+01      4.259369e+01  6.374381e+00  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_std.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x1600 with 29 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_data_features(X= data_std, data_cols= data_cols, y= y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dump New dataset to pickle file\n",
    "data_std[\"label\"] = y_train \n",
    "with open('../Data/UNSW-NB15/data_std.pickle', 'wb') as f:\n",
    "    pickle.dump(data_std,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
